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Deep learning with application to hashing

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Author

Zhang, Boshen

Date of Issue

2014

School

School of Computer Engineering

Abstract

Deep Learning and Learning to Hash are two important research areas in machine learning,
which have rapid improvements in recent years.
What I mainly researched on is an inter-discipline field: deep learning for cross view
hashing. Multiple layers of representation in deep learning has the property of abstracting
representation from input data, while, in the cross view similarity search, the biggest
difficulty is to represent items from one domain to another. Here, I want to take advantage
of the latest deep learning technology to solve the cross view similarity search problem.
Hashing is used to accelerate this process.
This thesis mainly contains three parts. Chapter 2 is a literature survey. It contains
a deep learning survey and a learning to hash survey. The deep learning survey briefly
introduces fundamental technology of deep learning and its recent development including
the latest technology. The Learning to Hash survey brief introduces some widely used
learning to hash algorithms. Chapter 3 is an experiment about comparison of some state
of the arts learning to hash algorithms. Chapter 4 is cross view hashing based on deep
learning. I present a cross view feature hashing technique using deep learning and show
some results. These three chapters are main chapters. Chapter 1 and Chapter 5 are
introduction and conclusion.